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          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
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            "display": null,
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            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
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            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
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        "107b9e88ed854bf680704227cfc2f950": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
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    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lyogavin/Anima/blob/main/air_llm/tests/test_notebooks/test_models_transformer_4_36_2_torch_2_1_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# install airllm and transformers torch"
      ],
      "metadata": {
        "id": "2b7k74ZdFwoA"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UXiPut421lAf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U airllm transformers torch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xgUac4sUGbDz",
        "outputId": "b161b35e-4353-4896-e35b-1e71bbbaf9ac"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting airllm\n",
            "  Downloading airllm-2.6-py3-none-any.whl (33 kB)\n",
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            "\u001b[?25hRequirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.1.0+cu121)\n",
            "Collecting torch\n",
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            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch)\n",
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            "\u001b[?25hCollecting nvidia-cuda-runtime-cu12==12.1.105 (from torch)\n",
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            "\u001b[?25hCollecting nvidia-cuda-cupti-cu12==12.1.105 (from torch)\n",
            "  Downloading nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.1/14.1 MB\u001b[0m \u001b[31m22.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-cudnn-cu12==8.9.2.26 (from torch)\n",
            "  Downloading nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
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            "\u001b[?25hCollecting nvidia-cublas-cu12==12.1.3.1 (from torch)\n",
            "  Downloading nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
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            "\u001b[?25hCollecting nvidia-cufft-cu12==11.0.2.54 (from torch)\n",
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            "\u001b[?25hCollecting nvidia-curand-cu12==10.3.2.106 (from torch)\n",
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            "\u001b[?25hCollecting nvidia-cusolver-cu12==11.4.5.107 (from torch)\n",
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            "\u001b[?25hCollecting nvidia-cusparse-cu12==12.1.0.106 (from torch)\n",
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            "\u001b[?25hCollecting nvidia-nccl-cu12==2.18.1 (from torch)\n",
            "  Downloading nvidia_nccl_cu12-2.18.1-py3-none-manylinux1_x86_64.whl (209.8 MB)\n",
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            "\u001b[?25hCollecting nvidia-nvtx-cu12==12.1.105 (from torch)\n",
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            "\u001b[?25hCollecting datasets (from optimum->airllm)\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m17.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets->optimum->airllm) (3.9.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->optimum->airllm) (23.1.0)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->optimum->airllm) (6.0.4)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->optimum->airllm) (1.9.4)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->optimum->airllm) (1.4.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->optimum->airllm) (1.3.1)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->optimum->airllm) (4.0.3)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets->optimum->airllm) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets->optimum->airllm) (2023.3.post1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets->optimum->airllm) (1.16.0)\n",
            "Installing collected packages: sentencepiece, pyarrow-hotfix, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, humanfriendly, dill, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, coloredlogs, nvidia-cusolver-cu12, transformers, torch, datasets, accelerate, optimum, airllm\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.35.2\n",
            "    Uninstalling transformers-4.35.2:\n",
            "      Successfully uninstalled transformers-4.35.2\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 2.1.0+cu121\n",
            "    Uninstalling torch-2.1.0+cu121:\n",
            "      Successfully uninstalled torch-2.1.0+cu121\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torchaudio 2.1.0+cu121 requires torch==2.1.0, but you have torch 2.1.2 which is incompatible.\n",
            "torchdata 0.7.0 requires torch==2.1.0, but you have torch 2.1.2 which is incompatible.\n",
            "torchtext 0.16.0 requires torch==2.1.0, but you have torch 2.1.2 which is incompatible.\n",
            "torchvision 0.16.0+cu121 requires torch==2.1.0, but you have torch 2.1.2 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed accelerate-0.25.0 airllm-2.6 coloredlogs-15.0.1 datasets-2.15.0 dill-0.3.7 humanfriendly-10.0 multiprocess-0.70.15 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.18.1 nvidia-nvjitlink-cu12-12.3.101 nvidia-nvtx-cu12-12.1.105 optimum-1.16.1 pyarrow-hotfix-0.6 sentencepiece-0.1.99 torch-2.1.2 transformers-4.36.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip list | grep transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ppWxaXVz2ues",
        "outputId": "52b8acd5-b5f7-4e8e-f13e-c5598dc3aeeb"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "transformers                     4.36.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip list | grep torch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0IB_Hk_N2xrX",
        "outputId": "dbfd1efb-8c9b-43bc-ccb1-4a40d7c8d640"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch                            2.1.2\n",
            "torchaudio                       2.1.0+cu121\n",
            "torchdata                        0.7.0\n",
            "torchsummary                     1.5.1\n",
            "torchtext                        0.16.0\n",
            "torchvision                      0.16.0+cu121\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# copy local code for debugging\n",
        "#!pip show airllm\n",
        "#!cp ./*.py /usr/local/lib/python3.10/dist-packages/airllm/\n",
        "#!rm ./airllm.py"
      ],
      "metadata": {
        "id": "BZAkVJczEQ-y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# test Platypus2"
      ],
      "metadata": {
        "id": "GBGevKQvEMi1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from airllm import AutoModel\n",
        "\n",
        "MAX_LENGTH = 128\n",
        "# could use hugging face model repo id:\n",
        "model = AutoModel.from_pretrained(\"garage-bAInd/Platypus2-7B\", profiling_mode=True)\n",
        "#model = AirLLMLlama2(\"garage-bAInd/Platypus2-7B\", profiling_mode=False)\n",
        "\n",
        "# or use model's local path...\n",
        "#model = AirLLMLlama2(\"/home/ubuntu/.cache/huggingface/hub/models--garage-bAInd--Platypus2-70B-instruct/snapshots/b585e74bcaae02e52665d9ac6d23f4d0dbc81a0f\")\n",
        "\n",
        "input_text = [\n",
        "        #'What is the capital of China?',\n",
        "        'I like',\n",
        "    ]\n",
        "\n",
        "input_tokens = model.tokenizer(input_text,\n",
        "    return_tensors=\"pt\",\n",
        "    return_attention_mask=False,\n",
        "    truncation=True,\n",
        "    max_length=MAX_LENGTH,\n",
        "    #padding=True\n",
        "    )\n",
        "\n",
        "generation_output = model.generate(\n",
        "    input_tokens['input_ids'].cuda(),\n",
        "    max_new_tokens=3,\n",
        "    use_cache=True,\n",
        "    return_dict_in_generate=True)\n",
        "\n",
        "model.tokenizer.decode(generation_output.sequences[0])"
      ],
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        },
        "id": "eIIw0Qy_GoZt",
        "outputId": "7f59c055-33c6-4e5f-dd24-f16c0ed482ac"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ">>>> cache_utils installed\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4ad20b1910bc4632afa7a8809985cc31"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
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          "metadata": {}
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            }
          },
          "metadata": {}
        },
        {
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            "attn imp: <class 'transformers.models.llama.modeling_llama.LlamaSdpaAttention'>\n",
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            "total time for load_safe_tensor_cpu_wait: 47.299771308898926\n",
            "total time for create_layer_from_state_dict: 3.165720224380493\n",
            "total time for kick_off_load_cpu: 0.002129077911376953\n",
            "total infer process time(including all above plus gpu compute): 24.8441\n",
            "total infer wall time(including all above plus gpu compute): 64.7308\n",
            "new version of transfomer, no need to use BetterTransformer, try setting attn impl to sdpa...\n",
            "attn imp: <class 'transformers.models.llama.modeling_llama.LlamaSdpaAttention'>\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "cuda:0: 100%|██████████| 35/35 [01:03<00:00,  1.82s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total time for load_safe_tensor: 0.9532725070376102\n",
            "total time for compression_time: 0.0008025910001094871\n",
            "total time for pin_memory_to_trigger_load: 58.17424297332764\n",
            "total time for load_safe_tensor_cpu_wait: 47.72071886062622\n",
            "total time for create_layer_from_state_dict: 3.1395955085754395\n",
            "total time for kick_off_load_cpu: 0.0021560192108154297\n",
            "total infer process time(including all above plus gpu compute): 24.8212\n",
            "total infer wall time(including all above plus gpu compute): 64.9236\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'<s> I like to think of'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# test mistral"
      ],
      "metadata": {
        "id": "jwnmFERfREyx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from airllm import AutoModel\n",
        "\n",
        "MAX_LENGTH = 128\n",
        "# could use hugging face model repo id:\n",
        "model = AutoModel.from_pretrained(\"mistralai/Mistral-7B-Instruct-v0.1\", profiling_mode=True)\n",
        "\n",
        "# or use model's local path...\n",
        "#model = AirLLMLlama2(\"/home/ubuntu/.cache/huggingface/hub/models--garage-bAInd--Platypus2-70B-instruct/snapshots/b585e74bcaae02e52665d9ac6d23f4d0dbc81a0f\")\n",
        "\n",
        "input_text = [\n",
        "        #'What is the capital of China?',\n",
        "        'I like',\n",
        "    ]\n",
        "\n",
        "input_tokens = model.tokenizer(input_text,\n",
        "    return_tensors=\"pt\",\n",
        "    return_attention_mask=False,\n",
        "    truncation=True,\n",
        "    max_length=MAX_LENGTH,\n",
        "    #padding=True\n",
        "    )\n",
        "\n",
        "generation_output = model.generate(\n",
        "    input_tokens['input_ids'].cuda(),\n",
        "    max_new_tokens=3,\n",
        "    use_cache=True,\n",
        "    return_dict_in_generate=True)\n",
        "\n",
        "model.tokenizer.decode(generation_output.sequences[0])"
      ],
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        "outputId": "2f9b926e-2247-43b3-fb9f-6bb441fef5ea"
      },
      "execution_count": 8,
      "outputs": [
        {
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        },
        {
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          "text": [
            "Loading shard 1/2\n",
            "saved as: /root/.cache/huggingface/hub/models--mistralai--Mistral-7B-Instruct-v0.1/snapshots/9ab9e76e2b09f9f29ea2d56aa5bd139e4445c59e/splitted_model/model.embed_tokens.safetensors\n"
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            "saved as: /root/.cache/huggingface/hub/models--mistralai--Mistral-7B-Instruct-v0.1/snapshots/9ab9e76e2b09f9f29ea2d56aa5bd139e4445c59e/splitted_model/model.layers.21.safetensors\n"
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            "Loading shard 2/2\n",
            "saved as: /root/.cache/huggingface/hub/models--mistralai--Mistral-7B-Instruct-v0.1/snapshots/9ab9e76e2b09f9f29ea2d56aa5bd139e4445c59e/splitted_model/model.layers.22.safetensors\n"
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            "saved as: /root/.cache/huggingface/hub/models--mistralai--Mistral-7B-Instruct-v0.1/snapshots/9ab9e76e2b09f9f29ea2d56aa5bd139e4445c59e/splitted_model/model.layers.30.safetensors\n"
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            "saved as: /root/.cache/huggingface/hub/models--mistralai--Mistral-7B-Instruct-v0.1/snapshots/9ab9e76e2b09f9f29ea2d56aa5bd139e4445c59e/splitted_model/model.layers.31.safetensors\n"
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            "saved as: /root/.cache/huggingface/hub/models--mistralai--Mistral-7B-Instruct-v0.1/snapshots/9ab9e76e2b09f9f29ea2d56aa5bd139e4445c59e/splitted_model/model.norm.safetensors\n"
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          "name": "stdout",
          "text": [
            "saved as: /root/.cache/huggingface/hub/models--mistralai--Mistral-7B-Instruct-v0.1/snapshots/9ab9e76e2b09f9f29ea2d56aa5bd139e4445c59e/splitted_model/lm_head.safetensors\n"
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          "text": [
            "100%|██████████| 35/35 [08:35<00:00, 14.73s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "either BetterTransformer or attn_implementation='sdpa' is available, creating model directly\n",
            "either BetterTransformer or attn_implementation='sdpa' is available, creating model directly\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "cuda:0: 100%|██████████| 35/35 [01:21<00:00,  2.33s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total time for load_safe_tensor: 1.1388072129673787\n",
            "total time for compression_time: 0.0014708880000284807\n",
            "total time for pin_memory_to_trigger_load: 63.38010597229004\n",
            "total time for load_safe_tensor_cpu_wait: 51.740307092666626\n",
            "total time for create_layer_from_state_dict: 16.00637412071228\n",
            "total time for kick_off_load_cpu: 0.002372264862060547\n",
            "total infer process time(including all above plus gpu compute): 43.4302\n",
            "total infer wall time(including all above plus gpu compute): 84.5998\n",
            "either BetterTransformer or attn_implementation='sdpa' is available, creating model directly\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "cuda:0: 100%|██████████| 35/35 [01:24<00:00,  2.41s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total time for load_safe_tensor: 1.0264794162849853\n",
            "total time for compression_time: 0.0012427040000488887\n",
            "total time for pin_memory_to_trigger_load: 65.2859718799591\n",
            "total time for load_safe_tensor_cpu_wait: 52.74174785614014\n",
            "total time for create_layer_from_state_dict: 16.73379683494568\n",
            "total time for kick_off_load_cpu: 0.0024042129516601562\n",
            "total infer process time(including all above plus gpu compute): 43.8728\n",
            "total infer wall time(including all above plus gpu compute): 86.7632\n",
            "either BetterTransformer or attn_implementation='sdpa' is available, creating model directly\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "cuda:0: 100%|██████████| 35/35 [01:22<00:00,  2.36s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total time for load_safe_tensor: 0.9650273855958176\n",
            "total time for compression_time: 0.001417822000007618\n",
            "total time for pin_memory_to_trigger_load: 64.69056248664856\n",
            "total time for load_safe_tensor_cpu_wait: 52.47505307197571\n",
            "total time for create_layer_from_state_dict: 15.874628782272339\n",
            "total time for kick_off_load_cpu: 0.0024225711822509766\n",
            "total infer process time(including all above plus gpu compute): 43.1137\n",
            "total infer wall time(including all above plus gpu compute): 85.3704\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'<s> I like to think of'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# test qwen"
      ],
      "metadata": {
        "id": "EsZ8RCaSmgSh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tiktoken einops transformers_stream_generator"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dHNRzbDNt6uc",
        "outputId": "1ad5f653-6cc0-411b-9eae-ab2fc47eab82"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting tiktoken\n",
            "  Downloading tiktoken-0.5.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting einops\n",
            "  Downloading einops-0.7.0-py3-none-any.whl (44 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.6/44.6 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting transformers_stream_generator\n",
            "  Downloading transformers-stream-generator-0.0.4.tar.gz (12 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: regex>=2022.1.18 in /usr/local/lib/python3.10/dist-packages (from tiktoken) (2023.6.3)\n",
            "Requirement already satisfied: requests>=2.26.0 in /usr/local/lib/python3.10/dist-packages (from tiktoken) (2.31.0)\n",
            "Requirement already satisfied: transformers>=4.26.1 in /usr/local/lib/python3.10/dist-packages (from transformers_stream_generator) (4.36.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.26.0->tiktoken) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.26.0->tiktoken) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.26.0->tiktoken) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.26.0->tiktoken) (2023.11.17)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers>=4.26.1->transformers_stream_generator) (3.13.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.26.1->transformers_stream_generator) (0.19.4)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.26.1->transformers_stream_generator) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.26.1->transformers_stream_generator) (23.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.26.1->transformers_stream_generator) (6.0.1)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.26.1->transformers_stream_generator) (0.15.0)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.26.1->transformers_stream_generator) (0.4.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.26.1->transformers_stream_generator) (4.66.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers>=4.26.1->transformers_stream_generator) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers>=4.26.1->transformers_stream_generator) (4.5.0)\n",
            "Building wheels for collected packages: transformers_stream_generator\n",
            "  Building wheel for transformers_stream_generator (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for transformers_stream_generator: filename=transformers_stream_generator-0.0.4-py3-none-any.whl size=12316 sha256=d5767bd8bfc8932ede6d8f73671ea9fb2295a8d66cbd54f657c48ab3b77f0c93\n",
            "  Stored in directory: /root/.cache/pip/wheels/47/1d/3c/92d88493ed40c0d9be60a391eb76c9a56e9f9b7542cb789401\n",
            "Successfully built transformers_stream_generator\n",
            "Installing collected packages: einops, tiktoken, transformers_stream_generator\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "llmx 0.0.15a0 requires cohere, which is not installed.\n",
            "llmx 0.0.15a0 requires openai, which is not installed.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed einops-0.7.0 tiktoken-0.5.2 transformers_stream_generator-0.0.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from airllm import AutoModel\n",
        "\n",
        "MAX_LENGTH = 128\n",
        "# could use hugging face model repo id:\n",
        "model = AutoModel.from_pretrained(\"Qwen/Qwen-7B\", profiling_mode=True)\n",
        "\n",
        "# or use model's local path...\n",
        "#model = AirLLMLlama2(\"/home/ubuntu/.cache/huggingface/hub/models--garage-bAInd--Platypus2-70B-instruct/snapshots/b585e74bcaae02e52665d9ac6d23f4d0dbc81a0f\")\n",
        "\n",
        "input_text = [\n",
        "        #'What is the capital of China?',\n",
        "        'I like',\n",
        "    ]\n",
        "\n",
        "input_tokens = model.tokenizer(input_text,\n",
        "    return_tensors=\"pt\",\n",
        "    return_attention_mask=False,\n",
        "    truncation=True,\n",
        "    max_length=MAX_LENGTH,\n",
        "    #padding=True\n",
        "    )\n",
        "\n",
        "generation_output = model.generate(\n",
        "    input_tokens['input_ids'].cuda(),\n",
        "    max_new_tokens=3,\n",
        "    use_cache=True,\n",
        "    return_dict_in_generate=True)\n",
        "\n",
        "model.tokenizer.decode(generation_output.sequences[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        "outputId": "09bcb2ef-2d35-413a-9b86-44e9e15926db"
      },
      "execution_count": 2,
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        {
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          "name": "stdout",
          "text": [
            ">>>> cache_utils installed\n"
          ]
        },
        {
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          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/Qwen/Qwen-7B:\n",
            "- configuration_qwen.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
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            "Loading shard 1/8\n",
            "saved as: /root/.cache/huggingface/hub/models--Qwen--Qwen-7B/snapshots/ffe04dd57f85293043ba999a2c0daa788d6182e9/splitted_model/transformer.wte.safetensors\n"
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            "saved as: /root/.cache/huggingface/hub/models--Qwen--Qwen-7B/snapshots/ffe04dd57f85293043ba999a2c0daa788d6182e9/splitted_model/transformer.h.25.safetensors\n"
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          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "either BetterTransformer or attn_implementation='sdpa' is available, creating model directly\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:transformers_modules.ffe04dd57f85293043ba999a2c0daa788d6182e9.modeling_qwen:The model is automatically converting to fp16 for faster inference. If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\".\n",
            "WARNING:transformers_modules.ffe04dd57f85293043ba999a2c0daa788d6182e9.modeling_qwen:Try importing flash-attention for faster inference...\n",
            "WARNING:transformers_modules.ffe04dd57f85293043ba999a2c0daa788d6182e9.modeling_qwen:Warning: import flash_attn rotary fail, please install FlashAttention rotary to get higher efficiency https://github.com/Dao-AILab/flash-attention/tree/main/csrc/rotary\n",
            "WARNING:transformers_modules.ffe04dd57f85293043ba999a2c0daa788d6182e9.modeling_qwen:Warning: import flash_attn rms_norm fail, please install FlashAttention layer_norm to get higher efficiency https://github.com/Dao-AILab/flash-attention/tree/main/csrc/layer_norm\n",
            "WARNING:transformers_modules.ffe04dd57f85293043ba999a2c0daa788d6182e9.modeling_qwen:Warning: import flash_attn fail, please install FlashAttention to get higher efficiency https://github.com/Dao-AILab/flash-attention\n",
            "WARNING:transformers_modules.ffe04dd57f85293043ba999a2c0daa788d6182e9.modeling_qwen:The model is automatically converting to fp16 for faster inference. If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\".\n",
            "WARNING:transformers_modules.ffe04dd57f85293043ba999a2c0daa788d6182e9.modeling_qwen:Try importing flash-attention for faster inference...\n",
            "WARNING:transformers_modules.ffe04dd57f85293043ba999a2c0daa788d6182e9.modeling_qwen:Warning: import flash_attn rotary fail, please install FlashAttention rotary to get higher efficiency https://github.com/Dao-AILab/flash-attention/tree/main/csrc/rotary\n",
            "WARNING:transformers_modules.ffe04dd57f85293043ba999a2c0daa788d6182e9.modeling_qwen:Warning: import flash_attn rms_norm fail, please install FlashAttention layer_norm to get higher efficiency https://github.com/Dao-AILab/flash-attention/tree/main/csrc/layer_norm\n",
            "WARNING:transformers_modules.ffe04dd57f85293043ba999a2c0daa788d6182e9.modeling_qwen:Warning: import flash_attn fail, please install FlashAttention to get higher efficiency https://github.com/Dao-AILab/flash-attention\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "either BetterTransformer or attn_implementation='sdpa' is available, creating model directly\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "cuda:0: 100%|██████████| 35/35 [01:34<00:00,  2.69s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total time for load_safe_tensor: 0.635954048965857\n",
            "total time for compression_time: 0.0013006659999632575\n",
            "total time for pin_memory_to_trigger_load: 72.96000123023987\n",
            "total time for load_safe_tensor_cpu_wait: 58.72205710411072\n",
            "total time for create_layer_from_state_dict: 19.167551517486572\n",
            "total time for kick_off_load_cpu: 0.0027763843536376953\n",
            "total infer process time(including all above plus gpu compute): 47.8944\n",
            "total infer wall time(including all above plus gpu compute): 95.4785\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:transformers_modules.ffe04dd57f85293043ba999a2c0daa788d6182e9.modeling_qwen:The model is automatically converting to fp16 for faster inference. If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\".\n",
            "WARNING:transformers_modules.ffe04dd57f85293043ba999a2c0daa788d6182e9.modeling_qwen:Try importing flash-attention for faster inference...\n",
            "WARNING:transformers_modules.ffe04dd57f85293043ba999a2c0daa788d6182e9.modeling_qwen:Warning: import flash_attn rotary fail, please install FlashAttention rotary to get higher efficiency https://github.com/Dao-AILab/flash-attention/tree/main/csrc/rotary\n",
            "WARNING:transformers_modules.ffe04dd57f85293043ba999a2c0daa788d6182e9.modeling_qwen:Warning: import flash_attn rms_norm fail, please install FlashAttention layer_norm to get higher efficiency https://github.com/Dao-AILab/flash-attention/tree/main/csrc/layer_norm\n",
            "WARNING:transformers_modules.ffe04dd57f85293043ba999a2c0daa788d6182e9.modeling_qwen:Warning: import flash_attn fail, please install FlashAttention to get higher efficiency https://github.com/Dao-AILab/flash-attention\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "either BetterTransformer or attn_implementation='sdpa' is available, creating model directly\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "cuda:0: 100%|██████████| 35/35 [01:29<00:00,  2.55s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total time for load_safe_tensor: 1.0234462836738487\n",
            "total time for compression_time: 0.0008647819999794137\n",
            "total time for pin_memory_to_trigger_load: 68.5471408367157\n",
            "total time for load_safe_tensor_cpu_wait: 58.62926721572876\n",
            "total time for create_layer_from_state_dict: 18.05242419242859\n",
            "total time for kick_off_load_cpu: 0.0023763179779052734\n",
            "total infer process time(including all above plus gpu compute): 44.5294\n",
            "total infer wall time(including all above plus gpu compute): 90.0592\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:transformers_modules.ffe04dd57f85293043ba999a2c0daa788d6182e9.modeling_qwen:The model is automatically converting to fp16 for faster inference. If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\".\n",
            "WARNING:transformers_modules.ffe04dd57f85293043ba999a2c0daa788d6182e9.modeling_qwen:Try importing flash-attention for faster inference...\n",
            "WARNING:transformers_modules.ffe04dd57f85293043ba999a2c0daa788d6182e9.modeling_qwen:Warning: import flash_attn rotary fail, please install FlashAttention rotary to get higher efficiency https://github.com/Dao-AILab/flash-attention/tree/main/csrc/rotary\n",
            "WARNING:transformers_modules.ffe04dd57f85293043ba999a2c0daa788d6182e9.modeling_qwen:Warning: import flash_attn rms_norm fail, please install FlashAttention layer_norm to get higher efficiency https://github.com/Dao-AILab/flash-attention/tree/main/csrc/layer_norm\n",
            "WARNING:transformers_modules.ffe04dd57f85293043ba999a2c0daa788d6182e9.modeling_qwen:Warning: import flash_attn fail, please install FlashAttention to get higher efficiency https://github.com/Dao-AILab/flash-attention\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "either BetterTransformer or attn_implementation='sdpa' is available, creating model directly\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "cuda:0: 100%|██████████| 35/35 [01:27<00:00,  2.51s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total time for load_safe_tensor: 0.7400774621939945\n",
            "total time for compression_time: 0.0011680459999752202\n",
            "total time for pin_memory_to_trigger_load: 67.09800553321838\n",
            "total time for load_safe_tensor_cpu_wait: 56.22022771835327\n",
            "total time for create_layer_from_state_dict: 18.800523281097412\n",
            "total time for kick_off_load_cpu: 0.002422809600830078\n",
            "total infer process time(including all above plus gpu compute): 45.2402\n",
            "total infer wall time(including all above plus gpu compute): 88.8204\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'I like the way you'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    }
  ]
}